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Abstract Photovoltaic (PV) is essential for global carbon neutrality, it is imperative to forecast PV generation accurately for power operation. With the rapid growth of distributed PV sites, a scalable cloud service tends to play a vital role in PV forecasting to address the increasing cost of computing resources and data subscriptions. Such a scheme creates a possibility to further enhance forecasting performance by re‐using forecasting model, data, and computing resources all in the cloud. In order to achieve this goal, this work proposes a multi‐site PV forecasting system design with a message queue (MQ) and stream computing engine, where a hybrid neural network model is trained and continuously updated using real‐time data. A performance benchmark with up to 60 sites served simultaneously was performed to verify the scalability of the stream computing based approach. Moreover, after incremental updating of the forecasting model, a decrease in normalized root mean square error and normalized mean absolute error of PV forecasting were observed, demonstrating that better short‐term forecasting accuracy was achieved.

Electric vehicles (EVs) play a crucial role in the global transition towards decarbonization and renewable energy resources (RERs). As EVs gain popularity, this has resulted in various challenges for the power grid, such as an intensified peak-to-valley load differential, causing transformer overloading. Vehicle-to-grid (V2G) technology has emerged as a promising solution due to its controllable charging and discharging capabilities. Mature business schemes can incentivize the development of V2G technology. However, the business schemes of V2G technology are still unclear. Therefore, this paper provides a comprehensive review of the business schemes associated with V2G technology, especially focusing on its feasibility and challenges with respect to the electricity market. In this paper, several business schemes with respect to the electricity market are explored by conducting extensive literature reviews, including peak-to-valley arbitrage, the spot market, demand–response (DR), frequency regulation, voltage regulation, spinning reserve, and black start. Next, application scenarios and real-world use cases of the V2G technology’s business schemes are investigated. Furthermore, the challenges faced by the V2G technology’s business schemes are assessed by considering the technical, economical, and social aspects. By identifying these challenges, it is important to highlight the existing shortcomings and areas of interest for V2G technology’s research and development. This review contributes to a deeper understanding of V2G technology and its implications for the energy sector.

Abstract Interspecies hydrogen transfer (IHT) and direct interspecies electron transfer (DIET) are two syntrophy models for methanogenesis. Their relative importance in methanogenic environments is still unclear. Our recent discovery of a novel species Candidatus Geobacter eutrophica with the genetic potential of IHT and DIET may serve as a model species to address this knowledge gap. To experimentally demonstrate its DIET ability, we performed electrochemical enrichment of Ca. G. eutrophica-dominating communities under 0 and 0.4 V vs. Ag/AgCl based on the presumption that DIET and extracellular electron transfer (EET) share similar metabolic pathways. After three batches of enrichment, Geobacter OTU650, which was phylogenetically close to Ca. G. eutrophica, was outcompeted in the control but remained abundant and active under electrochemical stimulation, indicating Ca. G. eutrophica’s EET ability. The high-quality draft genome further showed high phylogenomic similarity with Ca. G. eutrophica, and the genes encoding outer membrane cytochromes and enzymes for hydrogen metabolism were actively expressed. A Bayesian network was trained with the genes encoding enzymes for alcohol metabolism, hydrogen metabolism, EET, and methanogenesis from dominant fermentative bacteria, Geobacter, and Methanobacterium. Methane production could not be accurately predicted when the genes for IHT were in silico knocked out, inferring its more important role in methanogenesis. The genomics-enabled machine learning modeling approach can provide predictive insights into the importance of IHT and DIET.

Fermentation of waste activated sludge (WAS) is an alternative approach to reduce solid wastes while providing valuable soluble products, such as volatile fatty acids and alcohols. This study systematically identified optimal fermentation conditions and key microbial populations by conducting two sets of experiments under different combinations of biochemical and physical parameters. Based on fermentation product concentrations, methane production, and solid removal, fermentation performance was enhanced under the combined treatments of inoculum heat shock (>60°C), pH 5, 55°C, and short solid retention time (

Microbial community assembly in engineered biological systems is often simultaneously influenced by stochastic and deterministic processes, and the nexus of these two mechanisms remains to be further investigated. Here, three lab-scale activated sludge reactors were seeded with identical inoculum and operated in parallel under eight different sludge retention time (SRT) by sequentially reducing the SRT from 15 days to 1 day. Using 16S rRNA gene amplicon sequencing data, the microbial populations at the start-up (15-day SRT) and SRT-driven (≤10-day SRT) phases were observed to be noticeably different. Clustering results demonstrated ecological succession at the start-up phase with no consistent successional steps among the three reactors, suggesting that stochastic processes played an important role in the community assembly during primary succession. At the SRT-driven phase, the three reactors shared 31 core operational taxonomic units (OTUs). Putative primary acetate utilizers and secondary metabolizers were proposed based on K-means clustering, network and synchrony analysis. The shared core populations accounted for 65% of the total abundance, indicating that the microbial communities at the SRT-driven phase were shaped predominantly by deterministic processes. Sloan’s Neutral model and a null model analysis were performed to disentangle and quantify the relative influence of stochastic and deterministic processes on community assembly. The increased estimated migration rate in the neutral community model and the higher percentage of stochasticity in the null model implied that stochastic community assembly was intensified by strong deterministic factors. This was confirmed by the significantly different α- and β-diversity indices at SRTs shorter than 2 days and the observation that over half of the core OTUs were unshared or unsynchronized. Overall, this study provided quantitative insights into the nexus of stochastic and deterministic processes on microbial community assembly in a biological process.

Bioelectrochemical systems (BES) and forward osmosis (FO) are two emerging technologies with great potential for energy-efficient water/wastewater treatment. BES takes advantage of microbial interaction with a solid electron acceptor/donor to accomplish bioenergy recovery from organic compounds, and FO can extract high-quality water driven by an osmotic pressure. The strong synergy between those two technologies may complement each other and collaboratively address water-energy nexus. FO can assist BES with achieving water recovery (for future reuse), enhancing electricity generation, and supplying energy for accomplishing the cathode reactions; while BES may help FO with degrading organic contaminants, providing sustainable draw solute, and stabilizing water flux. This work has reviewed the recent development that focuses on the synergy between BES and FO, analyzed the advantages of each combination, and provided perspectives for future research. The findings encourage further investigation and development for efficient coordination between BES and FO towards an integrated system for wastewater treatment and reuse.